Overview
LEARN RSA Runwise AFNI (No Blur) – PI Walkthrough
This is a front-to-back walkthrough of what was run, where it lives, and how each step works. Each step includes file paths, commands, and full scripts used. The two key setbacks (timing fix and sub-1522 collinearity) are documented with audits and fixes.
Step 0 – Map of Paths and Environment
Paths used on server:
-
/data/projects/STUDIES/LEARN/fMRI/bids(raw BIDS inputs) -
/data/projects/STUDIES/LEARN/fMRI/RSA-learn/bids_fixed2(events after nopred_fdbk relabel) -
/data/projects/STUDIES/LEARN/fMRI/RSA-learn/TimingFiles/Fixed2(run-wise timing output) -
/data/projects/STUDIES/LEARN/fMRI/RSA-learn/derivatives/afni/IndvlLvlAnalyses(AFNI outputs) -
/data/projects/STUDIES/LEARN/fMRI/derivatives/afni/ssw(AFNI SSW anatomy) -
/data/projects/STUDIES/LEARN/fMRI/RSA-learn/scripts(server scripts)
Local repo:
-
/Users/dannyzweben/Desktop/SDN/Y1_project -
/Users/dannyzweben/Desktop/SDN/Y1_project/rsa-learn/scripts
AFNI version in logs: AFNI_25.1.11 (May 23 2025), afni_proc.py v7.92 (May 16 2025).
Step 1 – Fix events (nopred_fdbk relabel)
Purpose: some feedback events were labeled nopred_fdbk in BIDS events when a prediction was missed. This broke run-wise timing generation because those events did not match the expected peer×feedback labels. Fix = relabel those events using a canonical run template.
Command used:
python3 /data/projects/STUDIES/LEARN/fMRI/RSA-learn/scripts/LEARN_fix_nopred_fdbk_by_template.py \
--bids-dir /data/projects/STUDIES/LEARN/fMRI/RSA-learn/bids_fixed \
--out-dir /data/projects/STUDIES/LEARN/fMRI/RSA-learn/bids_fixed2 \
--report /data/projects/STUDIES/LEARN/fMRI/RSA-learn/reports/nopred_fdbk_fix_template.tsv \
--mode majority
Script (full): rsa-learn/scripts/LEARN_fix_nopred_fdbk_by_template.py
#!/usr/bin/env python3
"""
Relabel `nopred_fdbk` in BIDS events.tsv using the canonical
peer×feedback order derived from normal participants.
Two modes:
- majority: build a per-run template from the majority label at each trial
- subject: use a single normal subject as the template
"""
from __future__ import annotations
import argparse
import csv
from collections import Counter, defaultdict
from pathlib import Path
FEEDBACK = {
"Mean_60_fdkm",
"Mean_60_fdkn",
"Mean80_fdkm",
"Mean80_fdkn",
"Nice_60_fdkm",
"Nice_60_fdkn",
"Nice80_fdkm",
"Nice80_fdkn",
}
def find_events(bids_dir: Path):
return sorted(bids_dir.glob("sub-*/func/sub-*_task-learn_run-*_events.tsv"))
def sub_run_from_name(path: Path):
name = path.name
subj = name.split("_task-")[0].replace("sub-", "")
run = int(name.split("run-")[1].split("_events.tsv")[0])
return subj, run
def build_template_majority(bids_dir: Path):
counts = defaultdict(lambda: defaultdict(Counter))
for ev in find_events(bids_dir):
_, run = sub_run_from_name(ev)
with ev.open() as f:
header = f.readline().strip().split("\t")
if "event" not in header:
continue
i_event = header.index("event")
i_trial = header.index("trial")
for line in f:
if not line.strip():
continue
cols = line.rstrip("\n").split("\t")
event = cols[i_event]
if event not in FEEDBACK:
continue
trial = int(float(cols[i_trial]))
counts[run][trial][event] += 1
template = defaultdict(dict)
for run, trial_map in counts.items():
for trial, counter in trial_map.items():
if not counter:
continue
most_common = counter.most_common()
if len(most_common) > 1 and most_common[0][1] == most_common[1][1]:
continue
template[run][trial] = most_common[0][0]
return template
def build_template_from_subject(bids_dir: Path, subj: str):
template = defaultdict(dict)
for ev in find_events(bids_dir):
s, run = sub_run_from_name(ev)
if s != subj:
continue
with ev.open() as f:
header = f.readline().strip().split("\t")
if "event" not in header:
continue
i_event = header.index("event")
i_trial = header.index("trial")
for line in f:
if not line.strip():
continue
cols = line.rstrip("\n").split("\t")
event = cols[i_event]
if event not in FEEDBACK:
continue
trial = int(float(cols[i_trial]))
template[run][trial] = event
return template
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--bids-dir", required=True, type=Path)
ap.add_argument("--out-dir", required=True, type=Path)
ap.add_argument("--report", required=True, type=Path)
ap.add_argument("--mode", choices=["majority", "subject"], default="majority")
ap.add_argument("--template-subj", help="Required if mode=subject")
args = ap.parse_args()
if args.mode == "subject":
if not args.template_subj:
raise SystemExit("Need --template-subj when mode=subject")
template = build_template_from_subject(args.bids_dir, args.template_subj)
else:
template = build_template_majority(args.bids_dir)
report_rows = []
fixed = 0
unresolved = 0
total = 0
for ev in find_events(args.bids_dir):
rel = ev.relative_to(args.bids_dir)
out_path = args.out_dir / rel
out_path.parent.mkdir(parents=True, exist_ok=True)
with ev.open() as f:
header = f.readline().strip().split("\t")
if "event" not in header:
continue
i_event = header.index("event")
i_trial = header.index("trial")
rows = [r.rstrip("\n").split("\t") for r in f if r.strip()]
for row in rows:
if row[i_event] != "nopred_fdbk":
continue
total += 1
trial = int(float(row[i_trial]))
_, run = sub_run_from_name(ev)
new_event = template.get(run, {}).get(trial)
if new_event:
row[i_event] = new_event
fixed += 1
report_rows.append([*sub_run_from_name(ev), row[i_trial], "nopred_fdbk", new_event, "fixed"])
else:
unresolved += 1
report_rows.append([*sub_run_from_name(ev), row[i_trial], "nopred_fdbk", "NA", "unresolved"])
with out_path.open("w", newline="") as f:
writer = csv.writer(f, delimiter="\t")
writer.writerow(header)
writer.writerows(rows)
args.report.parent.mkdir(parents=True, exist_ok=True)
with args.report.open("w", newline="") as f:
writer = csv.writer(f, delimiter="\t")
writer.writerow(["subj", "run", "trial", "old_event", "new_event", "status"])
writer.writerows(report_rows)
print(f"[template_fix] total={total} fixed={fixed} unresolved={unresolved}")
if __name__ == "__main__":
main()
Outputs:
-
/data/projects/STUDIES/LEARN/fMRI/RSA-learn/bids_fixed2(corrected events) -
/data/projects/STUDIES/LEARN/fMRI/RSA-learn/reports/nopred_fdbk_fix_template.tsv(audit report)
Step 2 – Generate run-wise timing files (NonPM)
Purpose: convert corrected events into run-wise NonPM_*_runX.1D timing files and prediction/response timing files.
Important detail: the server copy of the timing script was hard-coded to bids and TimingFiles/Full. We patched a temporary copy to point at bids_fixed2 and TimingFiles/Fixed2.
Command used (temp patch + run):
TMP=/tmp/LEARN_1D_AFNItiming_Full_RSA_runwise_fixed2.sh
cp /data/projects/STUDIES/LEARN/fMRI/RSA-learn/scripts/LEARN_1D_AFNItiming_Full_RSA_runwise.sh "$TMP"
sed -i 's|BIDS_DIR="[^"]*"|BIDS_DIR="/data/projects/STUDIES/LEARN/fMRI/RSA-learn/bids_fixed2"|' "$TMP"
sed -i 's|TIMING_ROOT="[^"]*"|TIMING_ROOT="/data/projects/STUDIES/LEARN/fMRI/RSA-learn/TimingFiles/Fixed2"|' "$TMP"
bash "$TMP"
Script (full): rsa-learn/scripts/LEARN_1D_AFNItiming_Full_RSA_runwise.sh
#!/bin/bash
#######################################################
# SCRIPT SUMMARY
#######################################################
# RSA‑learn RUN‑WISE timing generator (NonPM only)
#
# This script is intentionally derived from:
# /data/projects/STUDIES/LEARN/fMRI/code/afni/LEARN_1D_AFNItiming_Full.sh
#
# Key goal:
# Create NON‑PARAMETRIC (onset:duration only) timing files
# for each run and each peer×feedback condition so we can
# estimate RUN‑WISE betas in AFNI.
#
# IMPORTANT: This script is *not* replacing the existing pipeline.
# It is a parallel RSA‑learn pipeline that matches the original
# naming conventions and event logic, but outputs run‑wise files.
#
# Author: RSA‑learn adaptation (based on Tessa Clarkson script)
# Date: 2026‑02‑08
############################################################################################
# GENERAL SETUP
############################################################################################
# **CHANGE ME**: Subject list file (one ID per line, no "sub-")
SUBJ_LIST="${SUBJ_LIST_OVERRIDE:-/data/projects/STUDIES/LEARN/fMRI/code/afni/subjList_LEARN.txt}"
# **CHECK ME**: Root directories
TOPDIR="/data/projects/STUDIES/LEARN/fMRI"
BIDS_DIR="${BIDS_DIR_OVERRIDE:-$TOPDIR/bids}"
# **RSA‑learn output root (new)**
TIMING_ROOT="${TIMING_ROOT_OVERRIDE:-$TOPDIR/RSA-learn/TimingFiles/Full}"
############################################################################################
# COPY EVENTS + BUILD NON‑PARAMETRIC RUN‑WISE TIMING FILES
############################################################################################
# For each subject...
for subj in `cat ${SUBJ_LIST}`; do
echo "[RSA‑learn] Generating NonPM run‑wise timing files for sub-${subj}"
# Create output folder (keep RSA‑learn timing separate)
mkdir -p "${TIMING_ROOT}/sub-${subj}"
# Clean any old event files so we know these are current
rm -f "${TIMING_ROOT}/sub-${subj}/sub-${subj}_task-learn_run-0"*_events.tsv
# Copy BIDS events into RSA‑learn timing folder (so everything is self‑contained)
cp "${BIDS_DIR}/sub-${subj}/func/sub-${subj}_task-learn_run-01_events.tsv" "${TIMING_ROOT}/sub-${subj}/"
cp "${BIDS_DIR}/sub-${subj}/func/sub-${subj}_task-learn_run-02_events.tsv" "${TIMING_ROOT}/sub-${subj}/"
cp "${BIDS_DIR}/sub-${subj}/func/sub-${subj}_task-learn_run-03_events.tsv" "${TIMING_ROOT}/sub-${subj}/"
cp "${BIDS_DIR}/sub-${subj}/func/sub-${subj}_task-learn_run-04_events.tsv" "${TIMING_ROOT}/sub-${subj}/"
# Move into subject timing folder
cd "${TIMING_ROOT}/sub-${subj}/"
############################################################
# NOTE ON NAMING
############################################################
# Event names in events.tsv use:
# Mean_60_* and Nice_60_* (underscore)
# Mean80_* and Nice80_* (no underscore)
# We preserve the existing naming convention for files:
# Mean60_*, Mean80_*, Nice60_*, Nice80_*
############################################################
############################################################
# NON‑PARAMETRIC FEEDBACK (peer × feedback) — RUN‑WISE
############################################################
# Mean_60 feedback
cat sub-${subj}_task-learn_run-01_events.tsv | awk '{if ($3=="Mean_60_fdkm") {printf "%s:%s ", $1, $2}}' > NonPM_Mean60_fdkm_run1.1D
cat sub-${subj}_task-learn_run-02_events.tsv | awk '{if ($3=="Mean_60_fdkm") {printf "%s:%s ", $1, $2}}' > NonPM_Mean60_fdkm_run2.1D
cat sub-${subj}_task-learn_run-03_events.tsv | awk '{if ($3=="Mean_60_fdkm") {printf "%s:%s ", $1, $2}}' > NonPM_Mean60_fdkm_run3.1D
cat sub-${subj}_task-learn_run-04_events.tsv | awk '{if ($3=="Mean_60_fdkm") {printf "%s:%s ", $1, $2}}' > NonPM_Mean60_fdkm_run4.1D
rm -f NonPM_Mean60_fdkm.1D
for f in NonPM_Mean60_fdkm_run1.1D NonPM_Mean60_fdkm_run2.1D NonPM_Mean60_fdkm_run3.1D NonPM_Mean60_fdkm_run4.1D; do (cat $f; echo '') >> NonPM_Mean60_fdkm.1D; done
cat sub-${subj}_task-learn_run-01_events.tsv | awk '{if ($3=="Mean_60_fdkn") {printf "%s:%s ", $1, $2}}' > NonPM_Mean60_fdkn_run1.1D
cat sub-${subj}_task-learn_run-02_events.tsv | awk '{if ($3=="Mean_60_fdkn") {printf "%s:%s ", $1, $2}}' > NonPM_Mean60_fdkn_run2.1D
cat sub-${subj}_task-learn_run-03_events.tsv | awk '{if ($3=="Mean_60_fdkn") {printf "%s:%s ", $1, $2}}' > NonPM_Mean60_fdkn_run3.1D
cat sub-${subj}_task-learn_run-04_events.tsv | awk '{if ($3=="Mean_60_fdkn") {printf "%s:%s ", $1, $2}}' > NonPM_Mean60_fdkn_run4.1D
rm -f NonPM_Mean60_fdkn.1D
for f in NonPM_Mean60_fdkn_run1.1D NonPM_Mean60_fdkn_run2.1D NonPM_Mean60_fdkn_run3.1D NonPM_Mean60_fdkn_run4.1D; do (cat $f; echo '') >> NonPM_Mean60_fdkn.1D; done
# Mean80 feedback
cat sub-${subj}_task-learn_run-01_events.tsv | awk '{if ($3=="Mean80_fdkm") {printf "%s:%s ", $1, $2}}' > NonPM_Mean80_fdkm_run1.1D
cat sub-${subj}_task-learn_run-02_events.tsv | awk '{if ($3=="Mean80_fdkm") {printf "%s:%s ", $1, $2}}' > NonPM_Mean80_fdkm_run2.1D
cat sub-${subj}_task-learn_run-03_events.tsv | awk '{if ($3=="Mean80_fdkm") {printf "%s:%s ", $1, $2}}' > NonPM_Mean80_fdkm_run3.1D
cat sub-${subj}_task-learn_run-04_events.tsv | awk '{if ($3=="Mean80_fdkm") {printf "%s:%s ", $1, $2}}' > NonPM_Mean80_fdkm_run4.1D
rm -f NonPM_Mean80_fdkm.1D
for f in NonPM_Mean80_fdkm_run1.1D NonPM_Mean80_fdkm_run2.1D NonPM_Mean80_fdkm_run3.1D NonPM_Mean80_fdkm_run4.1D; do (cat $f; echo '') >> NonPM_Mean80_fdkm.1D; done
cat sub-${subj}_task-learn_run-01_events.tsv | awk '{if ($3=="Mean80_fdkn") {printf "%s:%s ", $1, $2}}' > NonPM_Mean80_fdkn_run1.1D
cat sub-${subj}_task-learn_run-02_events.tsv | awk '{if ($3=="Mean80_fdkn") {printf "%s:%s ", $1, $2}}' > NonPM_Mean80_fdkn_run2.1D
cat sub-${subj}_task-learn_run-03_events.tsv | awk '{if ($3=="Mean80_fdkn") {printf "%s:%s ", $1, $2}}' > NonPM_Mean80_fdkn_run3.1D
cat sub-${subj}_task-learn_run-04_events.tsv | awk '{if ($3=="Mean80_fdkn") {printf "%s:%s ", $1, $2}}' > NonPM_Mean80_fdkn_run4.1D
rm -f NonPM_Mean80_fdkn.1D
for f in NonPM_Mean80_fdkn_run1.1D NonPM_Mean80_fdkn_run2.1D NonPM_Mean80_fdkn_run3.1D NonPM_Mean80_fdkn_run4.1D; do (cat $f; echo '') >> NonPM_Mean80_fdkn.1D; done
# Nice_60 feedback
cat sub-${subj}_task-learn_run-01_events.tsv | awk '{if ($3=="Nice_60_fdkm") {printf "%s:%s ", $1, $2}}' > NonPM_Nice60_fdkm_run1.1D
cat sub-${subj}_task-learn_run-02_events.tsv | awk '{if ($3=="Nice_60_fdkm") {printf "%s:%s ", $1, $2}}' > NonPM_Nice60_fdkm_run2.1D
cat sub-${subj}_task-learn_run-03_events.tsv | awk '{if ($3=="Nice_60_fdkm") {printf "%s:%s ", $1, $2}}' > NonPM_Nice60_fdkm_run3.1D
cat sub-${subj}_task-learn_run-04_events.tsv | awk '{if ($3=="Nice_60_fdkm") {printf "%s:%s ", $1, $2}}' > NonPM_Nice60_fdkm_run4.1D
rm -f NonPM_Nice60_fdkm.1D
for f in NonPM_Nice60_fdkm_run1.1D NonPM_Nice60_fdkm_run2.1D NonPM_Nice60_fdkm_run3.1D NonPM_Nice60_fdkm_run4.1D; do (cat $f; echo '') >> NonPM_Nice60_fdkm.1D; done
cat sub-${subj}_task-learn_run-01_events.tsv | awk '{if ($3=="Nice_60_fdkn") {printf "%s:%s ", $1, $2}}' > NonPM_Nice60_fdkn_run1.1D
cat sub-${subj}_task-learn_run-02_events.tsv | awk '{if ($3=="Nice_60_fdkn") {printf "%s:%s ", $1, $2}}' > NonPM_Nice60_fdkn_run2.1D
cat sub-${subj}_task-learn_run-03_events.tsv | awk '{if ($3=="Nice_60_fdkn") {printf "%s:%s ", $1, $2}}' > NonPM_Nice60_fdkn_run3.1D
cat sub-${subj}_task-learn_run-04_events.tsv | awk '{if ($3=="Nice_60_fdkn") {printf "%s:%s ", $1, $2}}' > NonPM_Nice60_fdkn_run4.1D
rm -f NonPM_Nice60_fdkn.1D
for f in NonPM_Nice60_fdkn_run1.1D NonPM_Nice60_fdkn_run2.1D NonPM_Nice60_fdkn_run3.1D NonPM_Nice60_fdkn_run4.1D; do (cat $f; echo '') >> NonPM_Nice60_fdkn.1D; done
# Nice80 feedback
cat sub-${subj}_task-learn_run-01_events.tsv | awk '{if ($3=="Nice80_fdkm") {printf "%s:%s ", $1, $2}}' > NonPM_Nice80_fdkm_run1.1D
cat sub-${subj}_task-learn_run-02_events.tsv | awk '{if ($3=="Nice80_fdkm") {printf "%s:%s ", $1, $2}}' > NonPM_Nice80_fdkm_run2.1D
cat sub-${subj}_task-learn_run-03_events.tsv | awk '{if ($3=="Nice80_fdkm") {printf "%s:%s ", $1, $2}}' > NonPM_Nice80_fdkm_run3.1D
cat sub-${subj}_task-learn_run-04_events.tsv | awk '{if ($3=="Nice80_fdkm") {printf "%s:%s ", $1, $2}}' > NonPM_Nice80_fdkm_run4.1D
rm -f NonPM_Nice80_fdkm.1D
for f in NonPM_Nice80_fdkm_run1.1D NonPM_Nice80_fdkm_run2.1D NonPM_Nice80_fdkm_run3.1D NonPM_Nice80_fdkm_run4.1D; do (cat $f; echo '') >> NonPM_Nice80_fdkm.1D; done
cat sub-${subj}_task-learn_run-01_events.tsv | awk '{if ($3=="Nice80_fdkn") {printf "%s:%s ", $1, $2}}' > NonPM_Nice80_fdkn_run1.1D
cat sub-${subj}_task-learn_run-02_events.tsv | awk '{if ($3=="Nice80_fdkn") {printf "%s:%s ", $1, $2}}' > NonPM_Nice80_fdkn_run2.1D
cat sub-${subj}_task-learn_run-03_events.tsv | awk '{if ($3=="Nice80_fdkn") {printf "%s:%s ", $1, $2}}' > NonPM_Nice80_fdkn_run3.1D
cat sub-${subj}_task-learn_run-04_events.tsv | awk '{if ($3=="Nice80_fdkn") {printf "%s:%s ", $1, $2}}' > NonPM_Nice80_fdkn_run4.1D
rm -f NonPM_Nice80_fdkn.1D
for f in NonPM_Nice80_fdkn_run1.1D NonPM_Nice80_fdkn_run2.1D NonPM_Nice80_fdkn_run3.1D NonPM_Nice80_fdkn_run4.1D; do (cat $f; echo '') >> NonPM_Nice80_fdkn.1D; done
############################################################
# NON‑PARAMETRIC PREDICTION + RESPONSE (RUN‑WISE)
############################################################
# Mean_60 prediction/response
cat sub-${subj}_task-learn_run-01_events.tsv | awk '{if ($3=="Mean_60_pred") {printf "%s:%s ", $1, $2}}' > Mean60_pred_run1.1D
cat sub-${subj}_task-learn_run-02_events.tsv | awk '{if ($3=="Mean_60_pred") {printf "%s:%s ", $1, $2}}' > Mean60_pred_run2.1D
cat sub-${subj}_task-learn_run-03_events.tsv | awk '{if ($3=="Mean_60_pred") {printf "%s:%s ", $1, $2}}' > Mean60_pred_run3.1D
cat sub-${subj}_task-learn_run-04_events.tsv | awk '{if ($3=="Mean_60_pred") {printf "%s:%s ", $1, $2}}' > Mean60_pred_run4.1D
rm -f Mean60_pred.1D
for f in Mean60_pred_run1.1D Mean60_pred_run2.1D Mean60_pred_run3.1D Mean60_pred_run4.1D; do (cat $f; echo '') >> Mean60_pred.1D; done
cat sub-${subj}_task-learn_run-01_events.tsv | awk '{if ($3=="Mean_60_rsp") {printf "%s:%s ", $1, $2}}' > Mean60_rsp_run1.1D
cat sub-${subj}_task-learn_run-02_events.tsv | awk '{if ($3=="Mean_60_rsp") {printf "%s:%s ", $1, $2}}' > Mean60_rsp_run2.1D
cat sub-${subj}_task-learn_run-03_events.tsv | awk '{if ($3=="Mean_60_rsp") {printf "%s:%s ", $1, $2}}' > Mean60_rsp_run3.1D
cat sub-${subj}_task-learn_run-04_events.tsv | awk '{if ($3=="Mean_60_rsp") {printf "%s:%s ", $1, $2}}' > Mean60_rsp_run4.1D
rm -f Mean60_rsp.1D
for f in Mean60_rsp_run1.1D Mean60_rsp_run2.1D Mean60_rsp_run3.1D Mean60_rsp_run4.1D; do (cat $f; echo '') >> Mean60_rsp.1D; done
# Mean80 prediction/response
cat sub-${subj}_task-learn_run-01_events.tsv | awk '{if ($3=="Mean80_pred") {printf "%s:%s ", $1, $2}}' > Mean80_pred_run1.1D
cat sub-${subj}_task-learn_run-02_events.tsv | awk '{if ($3=="Mean80_pred") {printf "%s:%s ", $1, $2}}' > Mean80_pred_run2.1D
cat sub-${subj}_task-learn_run-03_events.tsv | awk '{if ($3=="Mean80_pred") {printf "%s:%s ", $1, $2}}' > Mean80_pred_run3.1D
cat sub-${subj}_task-learn_run-04_events.tsv | awk '{if ($3=="Mean80_pred") {printf "%s:%s ", $1, $2}}' > Mean80_pred_run4.1D
rm -f Mean80_pred.1D
for f in Mean80_pred_run1.1D Mean80_pred_run2.1D Mean80_pred_run3.1D Mean80_pred_run4.1D; do (cat $f; echo '') >> Mean80_pred.1D; done
cat sub-${subj}_task-learn_run-01_events.tsv | awk '{if ($3=="Mean80_rsp") {printf "%s:%s ", $1, $2}}' > Mean80_rsp_run1.1D
cat sub-${subj}_task-learn_run-02_events.tsv | awk '{if ($3=="Mean80_rsp") {printf "%s:%s ", $1, $2}}' > Mean80_rsp_run2.1D
cat sub-${subj}_task-learn_run-03_events.tsv | awk '{if ($3=="Mean80_rsp") {printf "%s:%s ", $1, $2}}' > Mean80_rsp_run3.1D
cat sub-${subj}_task-learn_run-04_events.tsv | awk '{if ($3=="Mean80_rsp") {printf "%s:%s ", $1, $2}}' > Mean80_rsp_run4.1D
rm -f Mean80_rsp.1D
for f in Mean80_rsp_run1.1D Mean80_rsp_run2.1D Mean80_rsp_run3.1D Mean80_rsp_run4.1D; do (cat $f; echo '') >> Mean80_rsp.1D; done
# Nice_60 prediction/response
cat sub-${subj}_task-learn_run-01_events.tsv | awk '{if ($3=="Nice_60_pred") {printf "%s:%s ", $1, $2}}' > Nice60_pred_run1.1D
cat sub-${subj}_task-learn_run-02_events.tsv | awk '{if ($3=="Nice_60_pred") {printf "%s:%s ", $1, $2}}' > Nice60_pred_run2.1D
cat sub-${subj}_task-learn_run-03_events.tsv | awk '{if ($3=="Nice_60_pred") {printf "%s:%s ", $1, $2}}' > Nice60_pred_run3.1D
cat sub-${subj}_task-learn_run-04_events.tsv | awk '{if ($3=="Nice_60_pred") {printf "%s:%s ", $1, $2}}' > Nice60_pred_run4.1D
rm -f Nice60_pred.1D
for f in Nice60_pred_run1.1D Nice60_pred_run2.1D Nice60_pred_run3.1D Nice60_pred_run4.1D; do (cat $f; echo '') >> Nice60_pred.1D; done
cat sub-${subj}_task-learn_run-01_events.tsv | awk '{if ($3=="Nice_60_rsp") {printf "%s:%s ", $1, $2}}' > Nice60_rsp_run1.1D
cat sub-${subj}_task-learn_run-02_events.tsv | awk '{if ($3=="Nice_60_rsp") {printf "%s:%s ", $1, $2}}' > Nice60_rsp_run2.1D
cat sub-${subj}_task-learn_run-03_events.tsv | awk '{if ($3=="Nice_60_rsp") {printf "%s:%s ", $1, $2}}' > Nice60_rsp_run3.1D
cat sub-${subj}_task-learn_run-04_events.tsv | awk '{if ($3=="Nice_60_rsp") {printf "%s:%s ", $1, $2}}' > Nice60_rsp_run4.1D
rm -f Nice60_rsp.1D
for f in Nice60_rsp_run1.1D Nice60_rsp_run2.1D Nice60_rsp_run3.1D Nice60_rsp_run4.1D; do (cat $f; echo '') >> Nice60_rsp.1D; done
# Nice80 prediction/response
cat sub-${subj}_task-learn_run-01_events.tsv | awk '{if ($3=="Nice80_pred") {printf "%s:%s ", $1, $2}}' > Nice80_pred_run1.1D
cat sub-${subj}_task-learn_run-02_events.tsv | awk '{if ($3=="Nice80_pred") {printf "%s:%s ", $1, $2}}' > Nice80_pred_run2.1D
cat sub-${subj}_task-learn_run-03_events.tsv | awk '{if ($3=="Nice80_pred") {printf "%s:%s ", $1, $2}}' > Nice80_pred_run3.1D
cat sub-${subj}_task-learn_run-04_events.tsv | awk '{if ($3=="Nice80_pred") {printf "%s:%s ", $1, $2}}' > Nice80_pred_run4.1D
rm -f Nice80_pred.1D
for f in Nice80_pred_run1.1D Nice80_pred_run2.1D Nice80_pred_run3.1D Nice80_pred_run4.1D; do (cat $f; echo '') >> Nice80_pred.1D; done
cat sub-${subj}_task-learn_run-01_events.tsv | awk '{if ($3=="Nice80_rsp") {printf "%s:%s ", $1, $2}}' > Nice80_rsp_run1.1D
cat sub-${subj}_task-learn_run-02_events.tsv | awk '{if ($3=="Nice80_rsp") {printf "%s:%s ", $1, $2}}' > Nice80_rsp_run2.1D
cat sub-${subj}_task-learn_run-03_events.tsv | awk '{if ($3=="Nice80_rsp") {printf "%s:%s ", $1, $2}}' > Nice80_rsp_run3.1D
cat sub-${subj}_task-learn_run-04_events.tsv | awk '{if ($3=="Nice80_rsp") {printf "%s:%s ", $1, $2}}' > Nice80_rsp_run4.1D
rm -f Nice80_rsp.1D
for f in Nice80_rsp_run1.1D Nice80_rsp_run2.1D Nice80_rsp_run3.1D Nice80_rsp_run4.1D; do (cat $f; echo '') >> Nice80_rsp.1D; done
############################################################
# PAD RUN‑WISE NonPM FILES TO 4 ROWS (AFNI MULTI‑RUN)
############################################################
# AFNI expects one row per run for multi‑run datasets.
# Each run‑wise file currently has 1 row. Pad to 4 rows
# using '*' for non‑target runs.
for f in NonPM_*_run*.1D; do
run=$(echo "$f" | sed -E 's/.*_run([1-4])\.1D/\1/')
line=$(tr -d '
' < "$f")
if [ -z "$line" ]; then
line="*"
fi
case "$run" in
1) printf "%s
*
*
*
" "$line" > "$f" ;;
2) printf "*
%s
*
*
" "$line" > "$f" ;;
3) printf "*
*
%s
*
" "$line" > "$f" ;;
4) printf "*
*
*
%s
" "$line" > "$f" ;;
esac
done
done
Source script (lab original): /data/projects/STUDIES/LEARN/fMRI/code/afni/LEARN_1D_AFNItiming_Full.sh
The RSA timing script above is a run‑wise adaptation of this lab script. The original script is not reproduced here verbatim to avoid redundancy, but the full source is available at the path above.
Output example:
Example (run‑wise files for one condition; note * fillers for non‑target runs):
| Run 1 file | Run 2 file | Run 3 file | Run 4 file |
|---|---|---|---|
217.826:3 *** |
*32.757:3 356.689:3 ** |
**217.827:3 * |
***125.305:3 310.371:3 |
This shows how each run‑specific file contains timing only for its run, with * in the other three rows.
/data/projects/STUDIES/LEARN/fMRI/RSA-learn/TimingFiles/Fixed2/sub-1522/NonPM_Mean80_fdkn_run1.1D
Step 3 – Generate AFNI proc scripts (no blur) from raw BIDS
This step is not a new preprocessing design. It is a replica of the lab’s AFNI preprocessing script, with one intentional change for RSA: remove blur. Everything else (despike/tshift/align/tlrc/volreg/mask/scale/regress, censoring, SSW anatomy usage) stays aligned with the lab’s AFNI pipeline.
3.1 The lab AFNI preprocessing recipe we cloned
-
Source script (lab pipeline):
/data/projects/STUDIES/LEARN/fMRI/code/afni/LEARN_ap_Full_all.sh -
This is the canonical AFNI preprocessing recipe already used by the lab pipeline.
-
Same file via the mounted drive on my computer:
-
/Volumes/Jarcho_DataShare/projects/STUDIES/LEARN/fMRI/code/afni/LEARN_ap_Full_all.sh -
How to open it quickly:
less /data/projects/STUDIES/LEARN/fMRI/code/afni/LEARN_ap_Full_all.sh
# or (mounted drive)
less /Volumes/Jarcho_DataShare/projects/STUDIES/LEARN/fMRI/code/afni/LEARN_ap_Full_all.sh
Key lines in the lab script (show blur is included):
-blocks despike tshift align tlrc volreg blur mask scale regress \
...
-blur_size 6 \
3.2 The RSA adaptation (same pipeline, blur removed)
-
Script we ran:
rsa-learn/scripts/LEARN_ap_Full_RSA_runwise_AFNI_noblur.sh -
It is a direct adaptation of the lab script. The only changes were:
-
Inputs remain raw BIDS (
sub-<id>_task-learn_run-01_bold.nii.gz), matching the lab AFNI recipe. -
Timing inputs switched to run‑wise NonPM files under
TimingFiles/Fixed2. -
Blur removed (no
blurblock, no-blur_size).
How to verify the adaptation (diff vs lab script):
diff -u /data/projects/STUDIES/LEARN/fMRI/code/afni/LEARN_ap_Full_all.sh \
/data/projects/STUDIES/LEARN/fMRI/RSA-learn/scripts/LEARN_ap_Full_RSA_runwise_AFNI_noblur.sh | less
What you should see in the diff:
-
Timing file paths updated to
RSA-learn/TimingFiles/Fixed2/... -
The
blurblock removed and no-blur_size -
Everything else (despike/tshift/align/tlrc/volreg/mask/scale/regress + SSW anatomy usage) preserved
Key diff hunks (minimal):
- -blocks despike tshift align tlrc volreg blur mask scale regress \
- -blur_size 6 \
+ -blocks despike tshift align tlrc volreg mask scale regress \
- $stimdurmoddir/Mean60_fdkm.1D \
+ $stimdir/NonPM_Mean60_fdkm_run1.1D \
+ $stimdir/NonPM_Mean60_fdkm_run2.1D \
+ $stimdir/NonPM_Mean60_fdkm_run3.1D \
+ $stimdir/NonPM_Mean60_fdkm_run4.1D \
Proof of blur removal in the RSA script (actual line from the script):
-blocks despike tshift align tlrc volreg mask scale regress \
# (lab default includes 'blur' between volreg and mask)
There is no -blur_size line anywhere in this script.
3.3 What a “proc file” is, and when preprocessing vs GLM happens
-
afni_proc.pygenerates a proc file (a full AFNI script). -
That proc file runs preprocessing first (despike → tshift → align → tlrc → volreg → mask → scale), then runs the GLM (
3dDeconvolve/3dREMLfit) at the end. -
Running the proc file is what actually performs preprocessing and the GLM.
-
Example proc file location:
/data/projects/STUDIES/LEARN/fMRI/RSA-learn/derivatives/afni/IndvlLvlAnalyses/<id>/proc.<id>.LEARN_RSA_runwise_AFNI
Purpose: use the lab AFNI preprocessing on raw BIDS without blur, and embed the run‑wise timing + GLTs for RSA.
Script (full): rsa-learn/scripts/LEARN_ap_Full_RSA_runwise_AFNI_noblur.sh
#!/bin/tcsh
#######################################################
# SCRIPT SUMMARY
#######################################################
# RSA‑learn RUN‑WISE afni_proc generator (AFNI raw‑BIDS, NO smoothing)
#
# This script adapts the lab’s AFNI preprocessing pipeline to RSA run‑wise
# betas using raw BIDS inputs (not fMRIPrep). It removes the blur block to
# keep patterns unsmoothed for RSA.
#
# Author: RSA‑learn adaptation
# Date: 2026‑02‑14
############################################################################################
# GENERAL SETUP
############################################################################################
# **CHANGE ME**: Specify subject numbers in a single row. Do not include the sub- prefix
set subjects = ( 958 1158 1267 1380 )
# **CHECK ME**: GLM name (used for outputs)
set GLM = LEARN_RSA_runwise_AFNI
# **CHECK ME**: motion censor threshold (matches lab AFNI pipeline)
set motion_max = 1
# **CHECK ME**: Number of jobs for 3dDeconvolve
set jobs = 30
############################################################################################
# LOCATIONS
############################################################################################
set topdir = /data/projects/STUDIES/LEARN/fMRI
# Raw BIDS inputs
set subjbids = $topdir/bids
# RSA‑learn timing files (run‑wise NonPM)
set subjecttiming = $topdir/RSA-learn/TimingFiles/Full
# RSA‑learn output root
set results = $topdir/RSA-learn/derivatives/afni/IndvlLvlAnalyses
# AFNI SSW anatomy outputs
set anat_dir = $topdir/derivatives/afni/ssw
# Optional overrides
if ( $?BIDS_DIR_OVERRIDE ) set subjbids = $BIDS_DIR_OVERRIDE
if ( $?TIMING_ROOT_OVERRIDE ) set subjecttiming = $TIMING_ROOT_OVERRIDE
############################################################################################
# BEGIN
############################################################################################
cd $results
foreach subj ( $subjects )
mkdir -p $subj
cd $subj
set subj_dir = $subjbids/sub-$subj
set stimdir = $subjecttiming/sub-$subj
afni_proc.py -subj_id $subj \
-dsets \
$subj_dir/func/sub-${subj}_task-learn_run-01_bold.nii.gz \
$subj_dir/func/sub-${subj}_task-learn_run-02_bold.nii.gz \
$subj_dir/func/sub-${subj}_task-learn_run-03_bold.nii.gz \
$subj_dir/func/sub-${subj}_task-learn_run-04_bold.nii.gz \
-scr_overwrite \
-script $results/$subj/proc.$subj.$GLM \
-out_dir $subj.results.$GLM \
-blocks despike tshift align tlrc volreg mask scale regress \
-copy_anat $anat_dir/sub-${subj}/anatSS.$subj.nii \
-anat_has_skull no \
-anat_follower anat_w_skull anat $anat_dir/sub-${subj}/anatU.$subj.nii \
-mask_epi_anat yes \
-tlrc_base MNI152_2009_template_SSW.nii.gz \
-tshift_align_to -tzero 0 \
-align_opts_aea \
-giant_move \
-cost lpc+ZZ \
-AddEdge \
-anat_uniform_method unifize \
-tlrc_NL_warped_dsets \
$anat_dir/sub-${subj}/anatQQ.${subj}.nii \
$anat_dir/sub-${subj}/anatQQ.${subj}.aff12.1D \
$anat_dir/sub-${subj}/anatQQ.${subj}_WARP.nii \
-volreg_align_to MIN_OUTLIER \
-volreg_align_e2a \
-volreg_tlrc_warp \
-mask_dilate 1 \
-scale_max_val 200 \
-regress_censor_outliers 0.1 \
-regress_motion_per_run \
-regress_censor_motion $motion_max \
-regress_est_blur_epits \
-regress_est_blur_errts \
-regress_run_clustsim yes \
-html_review_style pythonic \
-test_stim_files no \
-regress_stim_times \
$stimdir/NonPM_Mean60_fdkm_run1.1D \
$stimdir/NonPM_Mean60_fdkn_run1.1D \
$stimdir/NonPM_Mean80_fdkm_run1.1D \
$stimdir/NonPM_Mean80_fdkn_run1.1D \
$stimdir/NonPM_Nice60_fdkm_run1.1D \
$stimdir/NonPM_Nice60_fdkn_run1.1D \
$stimdir/NonPM_Nice80_fdkm_run1.1D \
$stimdir/NonPM_Nice80_fdkn_run1.1D \
$stimdir/NonPM_Mean60_fdkm_run2.1D \
$stimdir/NonPM_Mean60_fdkn_run2.1D \
$stimdir/NonPM_Mean80_fdkm_run2.1D \
$stimdir/NonPM_Mean80_fdkn_run2.1D \
$stimdir/NonPM_Nice60_fdkm_run2.1D \
$stimdir/NonPM_Nice60_fdkn_run2.1D \
$stimdir/NonPM_Nice80_fdkm_run2.1D \
$stimdir/NonPM_Nice80_fdkn_run2.1D \
$stimdir/NonPM_Mean60_fdkm_run3.1D \
$stimdir/NonPM_Mean60_fdkn_run3.1D \
$stimdir/NonPM_Mean80_fdkm_run3.1D \
$stimdir/NonPM_Mean80_fdkn_run3.1D \
$stimdir/NonPM_Nice60_fdkm_run3.1D \
$stimdir/NonPM_Nice60_fdkn_run3.1D \
$stimdir/NonPM_Nice80_fdkm_run3.1D \
$stimdir/NonPM_Nice80_fdkn_run3.1D \
$stimdir/NonPM_Mean60_fdkm_run4.1D \
$stimdir/NonPM_Mean60_fdkn_run4.1D \
$stimdir/NonPM_Mean80_fdkm_run4.1D \
$stimdir/NonPM_Mean80_fdkn_run4.1D \
$stimdir/NonPM_Nice60_fdkm_run4.1D \
$stimdir/NonPM_Nice60_fdkn_run4.1D \
$stimdir/NonPM_Nice80_fdkm_run4.1D \
$stimdir/NonPM_Nice80_fdkn_run4.1D \
$stimdir/Mean60_pred.1D \
$stimdir/Mean60_rsp.1D \
$stimdir/Mean80_pred.1D \
$stimdir/Mean80_rsp.1D \
$stimdir/Nice60_pred.1D \
$stimdir/Nice60_rsp.1D \
$stimdir/Nice80_pred.1D \
$stimdir/Nice80_rsp.1D \
-regress_stim_labels \
FBM.Mean60.r1 \
FBN.Mean60.r1 \
FBM.Mean80.r1 \
FBN.Mean80.r1 \
FBM.Nice60.r1 \
FBN.Nice60.r1 \
FBM.Nice80.r1 \
FBN.Nice80.r1 \
FBM.Mean60.r2 \
FBN.Mean60.r2 \
FBM.Mean80.r2 \
FBN.Mean80.r2 \
FBM.Nice60.r2 \
FBN.Nice60.r2 \
FBM.Nice80.r2 \
FBN.Nice80.r2 \
FBM.Mean60.r3 \
FBN.Mean60.r3 \
FBM.Mean80.r3 \
FBN.Mean80.r3 \
FBM.Nice60.r3 \
FBN.Nice60.r3 \
FBM.Nice80.r3 \
FBN.Nice80.r3 \
FBM.Mean60.r4 \
FBN.Mean60.r4 \
FBM.Mean80.r4 \
FBN.Mean80.r4 \
FBM.Nice60.r4 \
FBN.Nice60.r4 \
FBM.Nice80.r4 \
FBN.Nice80.r4 \
Pred.Mean60 \
Resp.Mean60 \
Pred.Mean80 \
Resp.Mean80 \
Pred.Nice60 \
Resp.Nice60 \
Pred.Nice80 \
Resp.Nice80 \
-regress_stim_types \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
AM1 \
-regress_basis_multi \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
'dmBLOCK(0)' \
-regress_make_ideal_sum IDEAL_sum.1D \
-regress_opts_3dD \
-local_times \
-num_glt 45 \
-gltsym 'SYM: +FBM.Mean60.r1 +FBN.Mean60.r1 +FBM.Mean80.r1 +FBN.Mean80.r1 +FBM.Nice60.r1 +FBN.Nice60.r1 +FBM.Nice80.r1 +FBN.Nice80.r1 +FBM.Mean60.r2 +FBN.Mean60.r2 +FBM.Mean80.r2 +FBN.Mean80.r2 +FBM.Nice60.r2 +FBN.Nice60.r2 +FBM.Nice80.r2 +FBN.Nice80.r2 +FBM.Mean60.r3 +FBN.Mean60.r3 +FBM.Mean80.r3 +FBN.Mean80.r3 +FBM.Nice60.r3 +FBN.Nice60.r3 +FBM.Nice80.r3 +FBN.Nice80.r3 +FBM.Mean60.r4 +FBN.Mean60.r4 +FBM.Mean80.r4 +FBN.Mean80.r4 +FBM.Nice60.r4 +FBN.Nice60.r4 +FBM.Nice80.r4 +FBN.Nice80.r4 +Pred.Mean60 +Resp.Mean60 +Pred.Mean80 +Resp.Mean80 +Pred.Nice60 +Resp.Nice60 +Pred.Nice80 +Resp.Nice80' -glt_label 1 Task.V.BL \
-gltsym 'SYM: +Pred.Mean60 +Pred.Mean80 +Pred.Nice60 +Pred.Nice80' -glt_label 2 Prediction.V.BL \
-gltsym 'SYM: +Pred.Mean60 +Pred.Mean80 -Pred.Nice60 -Pred.Nice80' -glt_label 3 Prediction.Mean.V.Nice \
-gltsym 'SYM: +FBM.Mean60.r1 +FBN.Mean60.r1 +FBM.Mean80.r1 +FBN.Mean80.r1 +FBM.Nice60.r1 +FBN.Nice60.r1 +FBM.Nice80.r1 +FBN.Nice80.r1 +FBM.Mean60.r2 +FBN.Mean60.r2 +FBM.Mean80.r2 +FBN.Mean80.r2 +FBM.Nice60.r2 +FBN.Nice60.r2 +FBM.Nice80.r2 +FBN.Nice80.r2 +FBM.Mean60.r3 +FBN.Mean60.r3 +FBM.Mean80.r3 +FBN.Mean80.r3 +FBM.Nice60.r3 +FBN.Nice60.r3 +FBM.Nice80.r3 +FBN.Nice80.r3 +FBM.Mean60.r4 +FBN.Mean60.r4 +FBM.Mean80.r4 +FBN.Mean80.r4 +FBM.Nice60.r4 +FBN.Nice60.r4 +FBM.Nice80.r4 +FBN.Nice80.r4' -glt_label 4 FB.V.BL \
-gltsym 'SYM: +FBM.Mean60.r1 +FBM.Mean80.r1 +FBM.Nice60.r1 +FBM.Nice80.r1 +FBM.Mean60.r2 +FBM.Mean80.r2 +FBM.Nice60.r2 +FBM.Nice80.r2 +FBM.Mean60.r3 +FBM.Mean80.r3 +FBM.Nice60.r3 +FBM.Nice80.r3 +FBM.Mean60.r4 +FBM.Mean80.r4 +FBM.Nice60.r4 +FBM.Nice80.r4' -glt_label 5 FBM.V.BL \
-gltsym 'SYM: +FBN.Mean60.r1 +FBN.Mean80.r1 +FBN.Nice60.r1 +FBN.Nice80.r1 +FBN.Mean60.r2 +FBN.Mean80.r2 +FBN.Nice60.r2 +FBN.Nice80.r2 +FBN.Mean60.r3 +FBN.Mean80.r3 +FBN.Nice60.r3 +FBN.Nice80.r3 +FBN.Mean60.r4 +FBN.Mean80.r4 +FBN.Nice60.r4 +FBN.Nice80.r4' -glt_label 6 FBN.V.BL \
-gltsym 'SYM: +FBM.Mean60.r1 +FBM.Mean80.r1 +FBM.Nice60.r1 +FBM.Nice80.r1 +FBM.Mean60.r2 +FBM.Mean80.r2 +FBM.Nice60.r2 +FBM.Nice80.r2 +FBM.Mean60.r3 +FBM.Mean80.r3 +FBM.Nice60.r3 +FBM.Nice80.r3 +FBM.Mean60.r4 +FBM.Mean80.r4 +FBM.Nice60.r4 +FBM.Nice80.r4 -FBN.Mean60.r1 -FBN.Mean80.r1 -FBN.Nice60.r1 -FBN.Nice80.r1 -FBN.Mean60.r2 -FBN.Mean80.r2 -FBN.Nice60.r2 -FBN.Nice80.r2 -FBN.Mean60.r3 -FBN.Mean80.r3 -FBN.Nice60.r3 -FBN.Nice80.r3 -FBN.Mean60.r4 -FBN.Mean80.r4 -FBN.Nice60.r4 -FBN.Nice80.r4' -glt_label 7 FBM.V.FBN \
-gltsym 'SYM: +0.5*FBM.Mean60.r1 +0.5*FBN.Mean60.r1' -glt_label 8 Mean60.r1 \
-gltsym 'SYM: +0.5*FBM.Mean80.r1 +0.5*FBN.Mean80.r1' -glt_label 9 Mean80.r1 \
-gltsym 'SYM: +0.5*FBM.Nice60.r1 +0.5*FBN.Nice60.r1' -glt_label 10 Nice60.r1 \
-gltsym 'SYM: +0.5*FBM.Nice80.r1 +0.5*FBN.Nice80.r1' -glt_label 11 Nice80.r1 \
-gltsym 'SYM: +0.5*FBM.Mean60.r2 +0.5*FBN.Mean60.r2' -glt_label 12 Mean60.r2 \
-gltsym 'SYM: +0.5*FBM.Mean80.r2 +0.5*FBN.Mean80.r2' -glt_label 13 Mean80.r2 \
-gltsym 'SYM: +0.5*FBM.Nice60.r2 +0.5*FBN.Nice60.r2' -glt_label 14 Nice60.r2 \
-gltsym 'SYM: +0.5*FBM.Nice80.r2 +0.5*FBN.Nice80.r2' -glt_label 15 Nice80.r2 \
-gltsym 'SYM: +0.5*FBM.Mean60.r3 +0.5*FBN.Mean60.r3' -glt_label 16 Mean60.r3 \
-gltsym 'SYM: +0.5*FBM.Mean80.r3 +0.5*FBN.Mean80.r3' -glt_label 17 Mean80.r3 \
-gltsym 'SYM: +0.5*FBM.Nice60.r3 +0.5*FBN.Nice60.r3' -glt_label 18 Nice60.r3 \
-gltsym 'SYM: +0.5*FBM.Nice80.r3 +0.5*FBN.Nice80.r3' -glt_label 19 Nice80.r3 \
-gltsym 'SYM: +0.5*FBM.Mean60.r4 +0.5*FBN.Mean60.r4' -glt_label 20 Mean60.r4 \
-gltsym 'SYM: +0.5*FBM.Mean80.r4 +0.5*FBN.Mean80.r4' -glt_label 21 Mean80.r4 \
-gltsym 'SYM: +0.5*FBM.Nice60.r4 +0.5*FBN.Nice60.r4' -glt_label 22 Nice60.r4 \
-gltsym 'SYM: +0.5*FBM.Nice80.r4 +0.5*FBN.Nice80.r4' -glt_label 23 Nice80.r4 \
-gltsym 'SYM: +0.25*FBM.Mean60.r1 +0.25*FBM.Mean80.r1 +0.25*FBM.Nice60.r1 +0.25*FBM.Nice80.r1' -glt_label 24 FBM.r1 \
-gltsym 'SYM: +0.25*FBN.Mean60.r1 +0.25*FBN.Mean80.r1 +0.25*FBN.Nice60.r1 +0.25*FBN.Nice80.r1' -glt_label 25 FBN.r1 \
-gltsym 'SYM: +0.25*FBM.Mean60.r2 +0.25*FBM.Mean80.r2 +0.25*FBM.Nice60.r2 +0.25*FBM.Nice80.r2' -glt_label 26 FBM.r2 \
-gltsym 'SYM: +0.25*FBN.Mean60.r2 +0.25*FBN.Mean80.r2 +0.25*FBN.Nice60.r2 +0.25*FBN.Nice80.r2' -glt_label 27 FBN.r2 \
-gltsym 'SYM: +0.25*FBM.Mean60.r3 +0.25*FBM.Mean80.r3 +0.25*FBM.Nice60.r3 +0.25*FBM.Nice80.r3' -glt_label 28 FBM.r3 \
-gltsym 'SYM: +0.25*FBN.Mean60.r3 +0.25*FBN.Mean80.r3 +0.25*FBN.Nice60.r3 +0.25*FBN.Nice80.r3' -glt_label 29 FBN.r3 \
-gltsym 'SYM: +0.25*FBM.Mean60.r4 +0.25*FBM.Mean80.r4 +0.25*FBM.Nice60.r4 +0.25*FBM.Nice80.r4' -glt_label 30 FBM.r4 \
-gltsym 'SYM: +0.25*FBN.Mean60.r4 +0.25*FBN.Mean80.r4 +0.25*FBN.Nice60.r4 +0.25*FBN.Nice80.r4' -glt_label 31 FBN.r4 \
-gltsym 'SYM: +0.25*FBM.Mean60.r1 +0.25*FBM.Mean60.r2 +0.25*FBM.Mean60.r3 +0.25*FBM.Mean60.r4' -glt_label 32 FBM.Mean60.all \
-gltsym 'SYM: +0.25*FBN.Mean60.r1 +0.25*FBN.Mean60.r2 +0.25*FBN.Mean60.r3 +0.25*FBN.Mean60.r4' -glt_label 33 FBN.Mean60.all \
-gltsym 'SYM: +0.25*FBM.Mean80.r1 +0.25*FBM.Mean80.r2 +0.25*FBM.Mean80.r3 +0.25*FBM.Mean80.r4' -glt_label 34 FBM.Mean80.all \
-gltsym 'SYM: +0.25*FBN.Mean80.r1 +0.25*FBN.Mean80.r2 +0.25*FBN.Mean80.r3 +0.25*FBN.Mean80.r4' -glt_label 35 FBN.Mean80.all \
-gltsym 'SYM: +0.25*FBM.Nice60.r1 +0.25*FBM.Nice60.r2 +0.25*FBM.Nice60.r3 +0.25*FBM.Nice60.r4' -glt_label 36 FBM.Nice60.all \
-gltsym 'SYM: +0.25*FBN.Nice60.r1 +0.25*FBN.Nice60.r2 +0.25*FBN.Nice60.r3 +0.25*FBN.Nice60.r4' -glt_label 37 FBN.Nice60.all \
-gltsym 'SYM: +0.25*FBM.Nice80.r1 +0.25*FBM.Nice80.r2 +0.25*FBM.Nice80.r3 +0.25*FBM.Nice80.r4' -glt_label 38 FBM.Nice80.all \
-gltsym 'SYM: +0.25*FBN.Nice80.r1 +0.25*FBN.Nice80.r2 +0.25*FBN.Nice80.r3 +0.25*FBN.Nice80.r4' -glt_label 39 FBN.Nice80.all \
-gltsym 'SYM: +0.125*FBM.Mean60.r1 +0.125*FBN.Mean60.r1 +0.125*FBM.Mean60.r2 +0.125*FBN.Mean60.r2 +0.125*FBM.Mean60.r3 +0.125*FBN.Mean60.r3 +0.125*FBM.Mean60.r4 +0.125*FBN.Mean60.r4' -glt_label 40 Mean60.all \
-gltsym 'SYM: +0.125*FBM.Mean80.r1 +0.125*FBN.Mean80.r1 +0.125*FBM.Mean80.r2 +0.125*FBN.Mean80.r2 +0.125*FBM.Mean80.r3 +0.125*FBN.Mean80.r3 +0.125*FBM.Mean80.r4 +0.125*FBN.Mean80.r4' -glt_label 41 Mean80.all \
-gltsym 'SYM: +0.125*FBM.Nice60.r1 +0.125*FBN.Nice60.r1 +0.125*FBM.Nice60.r2 +0.125*FBN.Nice60.r2 +0.125*FBM.Nice60.r3 +0.125*FBN.Nice60.r3 +0.125*FBM.Nice60.r4 +0.125*FBN.Nice60.r4' -glt_label 42 Nice60.all \
-gltsym 'SYM: +0.125*FBM.Nice80.r1 +0.125*FBN.Nice80.r1 +0.125*FBM.Nice80.r2 +0.125*FBN.Nice80.r2 +0.125*FBM.Nice80.r3 +0.125*FBN.Nice80.r3 +0.125*FBM.Nice80.r4 +0.125*FBN.Nice80.r4' -glt_label 43 Nice80.all \
-gltsym 'SYM: +0.0625*FBM.Mean60.r1 +0.0625*FBM.Mean80.r1 +0.0625*FBM.Nice60.r1 +0.0625*FBM.Nice80.r1 +0.0625*FBM.Mean60.r2 +0.0625*FBM.Mean80.r2 +0.0625*FBM.Nice60.r2 +0.0625*FBM.Nice80.r2 +0.0625*FBM.Mean60.r3 +0.0625*FBM.Mean80.r3 +0.0625*FBM.Nice60.r3 +0.0625*FBM.Nice80.r3 +0.0625*FBM.Mean60.r4 +0.0625*FBM.Mean80.r4 +0.0625*FBM.Nice60.r4 +0.0625*FBM.Nice80.r4' -glt_label 44 FBM.all \
-gltsym 'SYM: +0.0625*FBN.Mean60.r1 +0.0625*FBN.Mean80.r1 +0.0625*FBN.Nice60.r1 +0.0625*FBN.Nice80.r1 +0.0625*FBN.Mean60.r2 +0.0625*FBN.Mean80.r2 +0.0625*FBN.Nice60.r2 +0.0625*FBN.Nice80.r2 +0.0625*FBN.Mean60.r3 +0.0625*FBN.Mean80.r3 +0.0625*FBN.Nice60.r3 +0.0625*FBN.Nice80.r3 +0.0625*FBN.Mean60.r4 +0.0625*FBN.Mean80.r4 +0.0625*FBN.Nice60.r4 +0.0625*FBN.Nice80.r4' -glt_label 45 FBN.all \
-cbucket cbucket.stats.$subj \
-jobs $jobs
cd ..
end
Key inputs in this script:
-
Raw BIDS runs:
/data/projects/STUDIES/LEARN/fMRI/bids/sub-<id>/func/sub-<id>_task-learn_run-01_bold.nii.gz(runs 01–04) -
AFNI SSW anatomy:
/data/projects/STUDIES/LEARN/fMRI/derivatives/afni/ssw/sub-<id>/anatSS.<id>.nii -
Run-wise timing:
/data/projects/STUDIES/LEARN/fMRI/RSA-learn/TimingFiles/Fixed2/sub-<id>/NonPM_*_runX.1D
Step 4 – Run availability audit (raw BIDS)
Purpose: verify whether any subjects truly have only 2–3 runs in raw BIDS, before deciding whether a fallback is needed.
Audit command (run on mounted BIDS):
python3 - <<'PY'
import glob, os, re
bids="/Volumes/Jarcho_DataShare/projects/STUDIES/LEARN/fMRI/bids"
subs=sorted([d for d in glob.glob(os.path.join(bids,"sub-*")) if os.path.isdir(d)])
missing=[]
for s in subs:
sid=os.path.basename(s).split("-")[1]
files=glob.glob(os.path.join(s,"func",f"sub-{sid}_task-learn_run-*_bold.nii.gz"))
runs=set()
for f in files:
m=re.search(r"run-(\\d+)_bold",f)
if m:
runs.add(int(m.group(1)))
if runs:
if len(runs) != 4:
missing.append((sid,sorted(runs)))
else:
missing.append((sid,[]))
print("n_subs",len(subs))
print("missing_count",len(missing))
for sid,runs in missing:
print(sid, runs)
PY
Audit result (raw BIDS run files):
-
n_subs 55 -
missing_count 2 -
1165 [1, 2, 3](missing run 4) -
1274 [1, 2](missing runs 3–4)
Why this did not necessarily crash earlier runs:
-
The orchestrator script checks run availability per subject.
-
If a subject has
<4runs, it invokes the fallback patcher to rewrite the proc script with only those runs (so AFNI doesn’t try to load missing data). -
This is why missing‑run subjects can still run without a hard error.
Optional fallback (only if a subject truly has <4 runs) The script below shows how a proc would be rewritten to only include the available runs and recompute GLTs. It was kept as a reference.
Fallback patch script (example only)
Script (full): `rsa-learn/scripts/LEARN_ap_fallback_patch_afni_raw.py`#!/usr/bin/env python3
from pathlib import Path
import sys
# Usage: LEARN_ap_fallback_patch_afni_raw.py <ap_tmp> <subj> <runs...>
def main():
if len(sys.argv) < 4:
raise SystemExit("Usage: LEARN_ap_fallback_patch_afni_raw.py <ap_tmp> <subj> <runs...>")
ap = Path(sys.argv[1])
subj = sys.argv[2]
runs = [int(r) for r in sys.argv[3:] if r.strip().isdigit()]
if not runs:
raise SystemExit("No runs provided")
stimdir = "$stimdir"
subj_dir = "$subj_dir"
stim_defs = [
("NonPM_Mean60_fdkm", "FBM.Mean60"),
("NonPM_Mean60_fdkn", "FBN.Mean60"),
("NonPM_Mean80_fdkm", "FBM.Mean80"),
("NonPM_Mean80_fdkn", "FBN.Mean80"),
("NonPM_Nice60_fdkm", "FBM.Nice60"),
("NonPM_Nice60_fdkn", "FBN.Nice60"),
("NonPM_Nice80_fdkm", "FBM.Nice80"),
("NonPM_Nice80_fdkn", "FBN.Nice80"),
]
pred_resp = [
("Mean60_pred", "Pred.Mean60"),
("Mean60_rsp", "Resp.Mean60"),
("Mean80_pred", "Pred.Mean80"),
("Mean80_rsp", "Resp.Mean80"),
("Nice60_pred", "Pred.Nice60"),
("Nice60_rsp", "Resp.Nice60"),
("Nice80_pred", "Pred.Nice80"),
("Nice80_rsp", "Resp.Nice80"),
]
text = ap.read_text()
lines = text.splitlines()
def replace_block(lines_in, start_key, end_key, new_lines):
out = []
i = 0
while i < len(lines_in):
line = lines_in[i]
if start_key in line:
out.append(line)
i += 1
while i < len(lines_in) and end_key not in lines_in[i]:
i += 1
out.extend(new_lines)
continue
if end_key in line:
out.append(line)
i += 1
continue
out.append(line)
i += 1
return out
def replace_stim_times(lines_in, stim_times_lines):
out = []
i = 0
while i < len(lines_in):
line = lines_in[i]
if line.lstrip().startswith("-regress_stim_times"):
out.append(line)
i += 1
while i < len(lines_in) and "-regress_stim_labels" not in lines_in[i]:
i += 1
out.extend(stim_times_lines)
continue
out.append(line)
i += 1
return out
def build_dsets():
out = []
for r in runs:
out.append(f"\t\t\t{subj_dir}/func/sub-{subj}_task-learn_run-{r:02d}_bold.nii.gz \\")
return out
stim_times = []
stim_labels = []
for r in runs:
for s, lab in stim_defs:
stim_times.append(f"\t\t{stimdir}/{s}_run{r}.1D \\")
stim_labels.append(f"\t\t{lab}.r{r} \\")
for s, lab in pred_resp:
stim_times.append(f"\t\t{stimdir}/{s}.1D \\")
stim_labels.append(f"\t\t{lab} \\")
stim_count = len(stim_labels)
stim_types = ["\t\tAM1 \\"] * stim_count
basis_multi = ["\t\t'dmBLOCK(0)' \\"] * stim_count
lines2 = replace_block(lines, "-dsets", "-scr_overwrite", build_dsets())
lines2 = replace_stim_times(lines2, stim_times)
lines2 = replace_block(lines2, "-regress_stim_labels", "-regress_stim_types", stim_labels)
lines2 = replace_block(lines2, "-regress_stim_types", "-regress_basis_multi", stim_types)
lines2 = replace_block(lines2, "-regress_basis_multi", "-regress_make_ideal_sum", basis_multi)
# Ensure -regress_stim_times exists
if not any(l.lstrip().startswith("-regress_stim_times") for l in lines2):
for i, l in enumerate(lines2):
if "-test_stim_files" in l or "-regress_stim_times_offset" in l:
lines2.insert(i + 1, "\t\t-regress_stim_times \\")
break
def fmt_w(x):
return "" if abs(x - 1.0) < 1e-8 else f"{x:.6f}*"
def glt(sym, label, idx):
return f"\t\t-gltsym 'SYM: {sym}' -glt_label {idx} {label} \\"
runs_sorted = runs
num_runs = len(runs_sorted)
def all_run_terms(peer=None, cond=None):
terms = []
for r in runs_sorted:
if peer and cond:
terms.append(f"+{peer}.{cond}.r{r}")
elif peer:
for c in ["Mean60", "Mean80", "Nice60", "Nice80"]:
terms.append(f"+{peer}.{c}.r{r}")
else:
for p in ["FBM", "FBN"]:
for c in ["Mean60", "Mean80", "Nice60", "Nice80"]:
terms.append(f"+{p}.{c}.r{r}")
return terms
glt_lines = []
idx = 1
task_terms = all_run_terms() + [
"+Pred.Mean60", "+Resp.Mean60", "+Pred.Mean80", "+Resp.Mean80",
"+Pred.Nice60", "+Resp.Nice60", "+Pred.Nice80", "+Resp.Nice80",
]
glt_lines.append(glt(" ".join(task_terms), "Task.V.BL", idx)); idx += 1
glt_lines.append(glt("+Pred.Mean60 +Pred.Mean80 +Pred.Nice60 +Pred.Nice80", "Prediction.V.BL", idx)); idx += 1
glt_lines.append(glt("+Pred.Mean60 +Pred.Mean80 -Pred.Nice60 -Pred.Nice80", "Prediction.Mean.V.Nice", idx)); idx += 1
glt_lines.append(glt(" ".join(all_run_terms()), "FB.V.BL", idx)); idx += 1
glt_lines.append(glt(" ".join(all_run_terms(peer="FBM")), "FBM.V.BL", idx)); idx += 1
glt_lines.append(glt(" ".join(all_run_terms(peer="FBN")), "FBN.V.BL", idx)); idx += 1
fbm_terms = all_run_terms(peer="FBM")
fbn_terms = [t.replace("+", "-") for t in all_run_terms(peer="FBN")]
glt_lines.append(glt(" ".join(fbm_terms + fbn_terms), "FBM.V.FBN", idx)); idx += 1
for r in runs_sorted:
glt_lines.append(glt(f"+0.5*FBM.Mean60.r{r} +0.5*FBN.Mean60.r{r}", f"Mean60.r{r}", idx)); idx += 1
glt_lines.append(glt(f"+0.5*FBM.Mean80.r{r} +0.5*FBN.Mean80.r{r}", f"Mean80.r{r}", idx)); idx += 1
glt_lines.append(glt(f"+0.5*FBM.Nice60.r{r} +0.5*FBN.Nice60.r{r}", f"Nice60.r{r}", idx)); idx += 1
glt_lines.append(glt(f"+0.5*FBM.Nice80.r{r} +0.5*FBN.Nice80.r{r}", f"Nice80.r{r}", idx)); idx += 1
for r in runs_sorted:
glt_lines.append(glt(f"+0.25*FBM.Mean60.r{r} +0.25*FBM.Mean80.r{r} +0.25*FBM.Nice60.r{r} +0.25*FBM.Nice80.r{r}", f"FBM.r{r}", idx)); idx += 1
glt_lines.append(glt(f"+0.25*FBN.Mean60.r{r} +0.25*FBN.Mean80.r{r} +0.25*FBN.Nice60.r{r} +0.25*FBN.Nice80.r{r}", f"FBN.r{r}", idx)); idx += 1
wr = 1.0 / num_runs
for cond in ["Mean60", "Mean80", "Nice60", "Nice80"]:
fbm = " ".join([f"+{fmt_w(wr)}FBM.{cond}.r{r}" for r in runs_sorted])
fbn = " ".join([f"+{fmt_w(wr)}FBN.{cond}.r{r}" for r in runs_sorted])
glt_lines.append(glt(fbm, f"FBM.{cond}.all", idx)); idx += 1
glt_lines.append(glt(fbn, f"FBN.{cond}.all", idx)); idx += 1
wpr = 1.0 / (2 * num_runs)
for cond in ["Mean60", "Mean80", "Nice60", "Nice80"]:
terms = []
for r in runs_sorted:
terms.append(f"+{fmt_w(wpr)}FBM.{cond}.r{r}")
terms.append(f"+{fmt_w(wpr)}FBN.{cond}.r{r}")
glt_lines.append(glt(" ".join(terms), f"{cond}.all", idx)); idx += 1
wfb = 1.0 / (4 * num_runs)
fbm_terms = []
fbn_terms = []
for r in runs_sorted:
for cond in ["Mean60", "Mean80", "Nice60", "Nice80"]:
fbm_terms.append(f"+{fmt_w(wfb)}FBM.{cond}.r{r}")
fbn_terms.append(f"+{fmt_w(wfb)}FBN.{cond}.r{r}")
glt_lines.append(glt(" ".join(fbm_terms), "FBM.all", idx)); idx += 1
glt_lines.append(glt(" ".join(fbn_terms), "FBN.all", idx)); idx += 1
filtered = []
inserted = False
for line in lines2:
if " -num_glt " in line or line.strip().startswith("-gltsym") or " -glt_label " in line:
continue
filtered.append(line)
if (not inserted) and line.strip().startswith("-local_times"):
filtered.append(f"\t\t-num_glt {len(glt_lines)} \\")
filtered.extend(glt_lines)
inserted = True
ap.write_text("\n".join(filtered) + "\n")
if __name__ == "__main__":
main()
Step 5 – Run the full AFNI pipeline (proc + GLM) in tmux
Purpose: run the AFNI proc scripts and GLM for all subjects using the fixed timing files.
How pre‑processing vs GLM happens in practice:
-
MAKE_PROC=1only generates theproc.<id>.LEARN_RSA_runwise_AFNIscripts (no data processed yet). -
RUN_GLM=1executes each proc script. Each proc script performs all preprocessing steps first, then runs the GLM (3dDeconvolve/3dREMLfit) in the same script.
Call chain (who calls what):
-
Orchestrator:
LEARN_run_RSA_runwise_pipeline_afni_raw.sh -
Generates a single‑subject proc via
LEARN_ap_Full_RSA_runwise_AFNI_noblur.sh -
Checks available runs from raw BIDS; if
<4, calls the fallback patcher to rewrite the proc -
If
RUN_GLM=1, runs the proc to do preprocessing + GLM
Minimal excerpt showing fallback invocation:
AP_FALLBACK="$SCRIPT_DIR/LEARN_ap_fallback_patch_afni_raw.py"
...
if [ "$run_count" -lt 4 ]; then
echo "[RSA-learn] FALLBACK (runs=${RUNS[*]}): $subj"
python3 "$AP_FALLBACK" "$AP_TMP" "$subj" ${RUNS[*]}
fi
Command used (tmux):
tmux kill-session -t rsa_afni
tmux new -s rsa_afni \
"SUBJ_ROOT=/data/projects/STUDIES/LEARN/fMRI/RSA-learn/TimingFiles/Fixed2 \
TIMING_ROOT_OVERRIDE=/data/projects/STUDIES/LEARN/fMRI/RSA-learn/TimingFiles/Fixed2 \
BIDS_DIR_OVERRIDE=/data/projects/STUDIES/LEARN/fMRI/bids \
MAKE_PROC=1 CLEAN_OUT=1 RUN_GLM=1 \
MAX_JOBS=16 LOAD_LIMIT=999 \
bash /data/projects/STUDIES/LEARN/fMRI/RSA-learn/scripts/LEARN_run_RSA_runwise_pipeline_afni_raw.sh"
Script (full): rsa-learn/scripts/LEARN_run_RSA_runwise_pipeline_afni_raw.sh
#!/bin/bash
#######################################################
# SCRIPT SUMMARY
#######################################################
# RSA‑learn RUN‑WISE pipeline (AFNI raw‑BIDS, NO smoothing)
#
# Standard workflow:
# 1) Generate afni_proc scripts (per subject)
# 2) Clean output directories (avoid "already exists")
# 3) Run the GLM from the correct working directory
#
# SUBJECT DISCOVERY:
# - No subject list required.
# - By default, discovers subjects from:
# $TIMING_ROOT/sub-*
# - If no timing folders found, falls back to:
# $BIDS_DIR/sub-*
#
# PARALLELIZATION:
# - Use MAX_JOBS to cap parallel subjects (default: CPU cores)
#
# FALLBACK:
# - If a subject has 2–3 runs, rewrite afni_proc inputs to those runs
# and recompute GLTs over available runs.
# - If a subject has <2 runs, skip.
#
# Usage:
# bash LEARN_run_RSA_runwise_pipeline_afni_raw.sh
# MAX_JOBS=4 bash LEARN_run_RSA_runwise_pipeline_afni_raw.sh
# SUBJ_ROOT=/path/to/sub-*/ bash LEARN_run_RSA_runwise_pipeline_afni_raw.sh
#
# Optional toggles (default = 1):
# MAKE_PROC=1 CLEAN_OUT=1 RUN_GLM=1
#
# Author: RSA‑learn adaptation
# Date: 2026‑02‑14
set -euo pipefail
TOPDIR="/data/projects/STUDIES/LEARN/fMRI"
RSA_DIR="$TOPDIR/RSA-learn"
SCRIPT_DIR="$RSA_DIR/scripts"
TMP_DIR="$RSA_DIR/tmp"
LOG_DIR="$RSA_DIR/logs"
RESULTS_DIR="$RSA_DIR/derivatives/afni/IndvlLvlAnalyses"
TIMING_ROOT="${TIMING_ROOT_OVERRIDE:-$RSA_DIR/TimingFiles/Full}"
BIDS_DIR="${BIDS_DIR_OVERRIDE:-$TOPDIR/bids}"
AP_ORIG="$SCRIPT_DIR/LEARN_ap_Full_RSA_runwise_AFNI_noblur.sh"
AP_FALLBACK="$SCRIPT_DIR/LEARN_ap_fallback_patch_afni_raw.py"
MAKE_PROC="${MAKE_PROC:-1}"
CLEAN_OUT="${CLEAN_OUT:-1}"
RUN_GLM="${RUN_GLM:-1}"
MAX_JOBS="${MAX_JOBS:-}"
LOAD_LIMIT="${LOAD_LIMIT:-}"
SUBJ_ROOT="${SUBJ_ROOT:-$TIMING_ROOT}"
mkdir -p "$TMP_DIR" "$LOG_DIR" "$RESULTS_DIR"
usage() {
cat <<EOF
Usage:
bash LEARN_run_RSA_runwise_pipeline_afni_raw.sh
Env:
MAX_JOBS=N # parallel subjects (default: CPU cores)
LOAD_LIMIT=N # 1-min loadavg threshold to start a new subject (default: MAX_JOBS)
SUBJ_ROOT=/path/to/sub-*/ # override discovery root
TIMING_ROOT_OVERRIDE=/path/to/timing
BIDS_DIR_OVERRIDE=/path/to/bids
Toggles:
MAKE_PROC=1 CLEAN_OUT=1 RUN_GLM=1
(set any to 0 to skip that step)
EOF
}
discover_subjects() {
local root="$1"
if [ ! -d "$root" ]; then
return 1
fi
find "$root" -maxdepth 1 -type d -name "sub-*" -printf "%f\n" 2>/dev/null \
| sed 's/^sub-//' | sort -u
}
if [ -z "${MAX_JOBS}" ]; then
if command -v nproc >/dev/null 2>&1; then
MAX_JOBS=$(nproc)
else
MAX_JOBS=$(getconf _NPROCESSORS_ONLN 2>/dev/null || echo 4)
fi
fi
if [ -z "${LOAD_LIMIT}" ]; then
LOAD_LIMIT="$MAX_JOBS"
fi
SUBJECTS=()
mapfile -t SUBJECTS < <(discover_subjects "$SUBJ_ROOT" || true)
if [ "${#SUBJECTS[@]}" -eq 0 ]; then
echo "[RSA-learn] No subjects found in $SUBJ_ROOT"
echo "[RSA-learn] Falling back to BIDS: $BIDS_DIR"
mapfile -t SUBJECTS < <(discover_subjects "$BIDS_DIR" || true)
fi
if [ "${#SUBJECTS[@]}" -eq 0 ]; then
echo "[RSA-learn] ERROR: No subjects discovered."
usage
exit 1
fi
echo "[RSA-learn] Found ${#SUBJECTS[@]} subjects."
echo "[RSA-learn] MAX_JOBS=$MAX_JOBS LOAD_LIMIT=$LOAD_LIMIT"
is_running() {
local subj="$1"
pgrep -f "proc\.${subj}\.LEARN_RSA_runwise_AFNI" >/dev/null 2>&1
}
proc_gen() {
local subj="$1"
if is_running "$subj"; then
echo "[RSA-learn] SKIP (running): $subj"
return 0
fi
if [ ! -f "$AP_ORIG" ]; then
echo "[RSA-learn] ERROR: Missing $AP_ORIG"
return 1
fi
AP_TMP="$TMP_DIR/LEARN_ap_Full_RSA_runwise_AFNI_${subj}.sh"
cp "$AP_ORIG" "$AP_TMP"
sed -i "s|^set subjects = .*|set subjects = ( ${subj} )|" "$AP_TMP"
mapfile -t RUNS < <(find "$BIDS_DIR/sub-${subj}/func" -maxdepth 1 -type f \
-name "sub-${subj}_task-learn_run-*_bold.nii.gz" 2>/dev/null \
| sed -E 's/.*run-0*([0-9]+).*/\1/' | sort -n)
local run_count="${#RUNS[@]}"
if [ "$run_count" -lt 2 ]; then
echo "[RSA-learn] SKIP (runs <2): $subj"
return 0
fi
if [ "$run_count" -lt 4 ]; then
echo "[RSA-learn] FALLBACK (runs=${RUNS[*]}): $subj"
python3 "$AP_FALLBACK" "$AP_TMP" "$subj" ${RUNS[*]}
fi
echo "[RSA-learn] PROC GEN: $subj"
tcsh "$AP_TMP" |& tee "$LOG_DIR/ap.${subj}.log"
}
clean_out() {
local subj="$1"
if is_running "$subj"; then
echo "[RSA-learn] SKIP CLEAN (running): $subj"
return 0
fi
OUT_BASE="$RESULTS_DIR/$subj"
OUT_DIR="$OUT_BASE/${subj}.results.LEARN_RSA_runwise_AFNI"
ALT_OUT_DIR="$SCRIPT_DIR/${subj}.results.LEARN_RSA_runwise_AFNI"
if [ -d "$OUT_DIR" ]; then
echo "[RSA-learn] CLEAN: $OUT_DIR"
rm -rf "$OUT_DIR"
fi
if [ -d "$ALT_OUT_DIR" ]; then
echo "[RSA-learn] CLEAN stray: $ALT_OUT_DIR"
rm -rf "$ALT_OUT_DIR"
fi
}
run_glm() {
local subj="$1"
if is_running "$subj"; then
echo "[RSA-learn] SKIP RUN (running): $subj"
return 0
fi
PROC="$RESULTS_DIR/$subj/proc.${subj}.LEARN_RSA_runwise_AFNI"
if [ ! -f "$PROC" ]; then
echo "[RSA-learn] MISSING PROC: $PROC"
return 0
fi
mkdir -p "$RESULTS_DIR/$subj"
echo "[RSA-learn] RUN: $subj"
( cd "$RESULTS_DIR/$subj" && tcsh -xef "proc.${subj}.LEARN_RSA_runwise_AFNI" |& tee "output.proc.${subj}.LEARN_RSA_runwise_AFNI" )
}
run_parallel() {
local fn="$1"; shift
local subj
if [ "$MAX_JOBS" -gt 1 ]; then
set -m
fi
wait_for_load() {
local limit="$1"
while true; do
local load
load=$(awk '{print $1}' /proc/loadavg 2>/dev/null || echo 0)
awk -v l="$load" -v t="$limit" 'BEGIN{exit !(l < t)}' && break
sleep 5
done
}
for subj in "$@"; do
while [ "$(jobs -pr | wc -l | tr -d ' ')" -ge "$MAX_JOBS" ]; do
sleep 5
done
wait_for_load "$LOAD_LIMIT"
"$fn" "$subj" &
done
wait
}
if [ "$MAKE_PROC" -eq 1 ]; then
run_parallel proc_gen "${SUBJECTS[@]}"
fi
if [ "$CLEAN_OUT" -eq 1 ]; then
run_parallel clean_out "${SUBJECTS[@]}"
fi
if [ "$RUN_GLM" -eq 1 ]; then
run_parallel run_glm "${SUBJECTS[@]}"
fi
Step 6 – Audit (post-run checks)
Purpose: confirm that all subjects produced stats outputs and catch failures quickly.
Commands used:
# how many finished
grep -R "execution finished" /data/projects/STUDIES/LEARN/fMRI/RSA-learn/derivatives/afni/IndvlLvlAnalyses/*/output.proc.*LEARN_RSA_runwise_AFNI | wc -l
# list missing stats
RESULTS=/data/projects/STUDIES/LEARN/fMRI/RSA-learn/derivatives/afni/IndvlLvlAnalyses
TIMING=/data/projects/STUDIES/LEARN/fMRI/RSA-learn/TimingFiles/Fixed2
for d in $TIMING/sub-*; do
id=${d##*sub-}
stats="$RESULTS/$id/${id}.results.LEARN_RSA_runwise_AFNI/stats.${id}+tlrc.HEAD"
[ ! -f "$stats" ] && echo "$id"
done | sort -n
# scan errors
egrep -R "ERROR|FATAL|FAILED|ABORT" \
/data/projects/STUDIES/LEARN/fMRI/RSA-learn/derivatives/afni/IndvlLvlAnalyses/*/output.proc.*LEARN_RSA_runwise_AFNI | head -n 50
Example beta map snapshots (sub‑1290) — click to expand
Four peer×feedback betas (one per run), rendered with `@chauffeur_afni` and saved as PNGs:| Condition | Axial | Coronal | Sagittal |
|---|---|---|---|
FBM.Mean60.r1 |
 |
 |
 |
FBN.Mean80.r2 |
 |
 |
 |
FBM.Nice60.r3 |
 |
 |
 |
FBN.Nice80.r4 |
 |
 |
 |
Setback B – sub-1522 GLM collinearity
Problem: 3dDeconvolve reported collinearity between FBN.Mean80.r1 and FBN.Mean80.r3 and stopped because -GOFORIT was not set. For sub‑1522, Mean80_fdkn onsets in run‑1 vs run‑3 were nearly identical (217.826 vs 217.827 s), making the run‑wise regressors almost the same after convolution.
Preprocessing had already completed (pb04..scale files exist), so the fix is GLM‑only*, not a full re‑preprocess.
Audit commands used:
id=1522
LOG=/data/projects/STUDIES/LEARN/fMRI/RSA-learn/derivatives/afni/IndvlLvlAnalyses/$id/output.proc.${id}.LEARN_RSA_runwise_AFNI
egrep -n "WARNING|ERROR|FATAL" "$LOG" | tail -n 30
sed -n '1,40p' /data/projects/STUDIES/LEARN/fMRI/RSA-learn/derivatives/afni/IndvlLvlAnalyses/1522/1522.results.LEARN_RSA_runwise_AFNI/3dDeconvolve.err
# confirm onsets in events
awk -F" " '$3=="Mean80_fdkn"{print $1}' /data/projects/STUDIES/LEARN/fMRI/RSA-learn/bids_fixed2/sub-1522/func/sub-1522_task-learn_run-01_events.tsv
awk -F" " '$3=="Mean80_fdkn"{print $1}' /data/projects/STUDIES/LEARN/fMRI/RSA-learn/bids_fixed2/sub-1522/func/sub-1522_task-learn_run-03_events.tsv
Fix + rerun (GLM‑only, in tmux):
tmux kill-session -t rsa_1522_glm 2>/dev/null
tmux new -s rsa_1522_glm "
set -e
id=1522
BASE=/data/projects/STUDIES/LEARN/fMRI/RSA-learn/derivatives/afni/IndvlLvlAnalyses/\$id
OUT=\$BASE/\${id}.results.LEARN_RSA_runwise_AFNI
PROC=\$BASE/proc.\${id}.LEARN_RSA_runwise_AFNI
# Ensure GOFORIT is in the 3dDeconvolve block (GLM only)
python3 - <<'PY'
from pathlib import Path
path = Path('$PROC')
lines = path.read_text().splitlines()
if any('GOFORIT' in l for l in lines):
raise SystemExit(0)
out = []
inserted = False
for l in lines:
out.append(l)
if (not inserted) and '-polort 3 -float' in l:
out.append(' -GOFORIT 1 \\\\')
inserted = True
if not inserted:
raise SystemExit('GOFORIT insertion point not found')
path.write_text('\\n'.join(out) + '\\n')
PY
# Clean GLM outputs only (keep preprocessing)
rm -f \$OUT/stats.\${id}+tlrc.* \$OUT/stats.\${id}_REML* \$OUT/cbucket* \$OUT/fitts* \$OUT/errts* \$OUT/X.* \$OUT/3dDeconvolve.err \
run_3dDeconvolve.body.tcsh run_3dDeconvolve.tcsh
cd \$OUT
awk 'BEGIN{p=0} /^3dDeconvolve /{p=1} p{if (\$0 ~ /^if \\( \\$status \\)/) {exit} else print}' \"\$PROC\" > run_3dDeconvolve.body.tcsh
{ echo \"set subj = 1522\"; cat run_3dDeconvolve.body.tcsh; } > run_3dDeconvolve.tcsh
tcsh -xef run_3dDeconvolve.tcsh |& tee 3dDeconvolve.rerun.log
tcsh -xef stats.REML_cmd |& tee 3dREMLfit.rerun.log
"